Various global or local communication networks (the Internet, the World Wide Web, local area networks and the like) offer a user a vast amount of information. The information includes a multitude of contextual topics, such as but not limited to, news and current affairs, maps, company information, financial information and resources, traffic information, games and entertainment related information. Users use a variety of client devices (desktop, laptop, notebook, smartphone, tablets and the like) to have access to rich content (like images, audio, video, animation, and other multimedia content from such networks).
Generally speaking, a given user can access a resource on the communication network by two principle means. The given user can access a particular resource directly, either by typing an address of the resource (typically an URL or Universal Resource Locator, such as www.webpage.com) or by clicking a link in an e-mail or in another web resource. Alternatively, the given user may conduct a search using a search engine to locate a resource of interest. The latter is particularly suitable in those circumstances, where the given user knows a topic of interest, but does not know the exact address of the resource he or she is interested in.
When the given user runs a search using the search engine, he or she generally has two priorities. He or she wants the search engine to locate the most relevant results and he or she wants the results relatively quickly. Search results are generally presented to the user on a webpage, such as a search engine results page (SERP). The SERP may contain any number of different types of results gathered from a variety of sources, such as general, textual search results from general internet searches, or particular types of search results (e.g., images) retrieved from vertical searches. Search engines use a variety of methods to determine which search results are most relevant in response to a search query, and how to display such results to the user. Despite the existence of ranking models to determine the selection and placement of search results on a SERP, improvements may still be made in methods and systems for ranking search results, to provide a more satisfactory search experience to the user.
U.S. Patent Application Publication No. 2011/0258149 published on Oct. 20, 2011 to Kanungo et al. teaches methods and computer-storage media having computer-executable instructions embodied thereon that facilitate generating a machine-learned model for ranking search results using click-based data. Data is referenced from user queries, which may include search results generated by general search engines and vertical search engines. A training set is generated from the search results and click-based judgments are associated with the search results in the training set. Based on click-based judgments, identifiable features are determined from the search results in a training set. Based on determining identifiable features in a training set, a rule set is generated for ranking subsequent search results.
International Patent Application Publication No. WO 2015/028898 published on Mar. 5, 2015 to Esinovskaya et al. teaches methods and systems for conducting a search and presenting results. The method comprises receiving a search query from an electronic device associated with a user; responsive to the search query, generating a search query result set, the search query result set including a vertical search result; determining a confidence level that the vertical search result is the most relevant to the search query; and, responsive to the confidence level being above a pre-determined threshold, causing the electronic device to display exclusively the vertical search result.
International Patent Application Publication No. WO 2015/056112 published on Apr. 23, 2015 to Karpovich et al. teaches methods and systems for determining a search response to a search query associated with a user. The method comprises determining the most relevant document to the search query by determining a likelihood parameter indicative of how likely the most relevant document is to satisfy the search query; in response to the likelihood being above a threshold, displaying exclusively the most relevant document; and, in response to the likelihood being below the threshold, displaying the general SERP including the most relevant document and other documents.